CN111709354A - Method and device for identifying target area, electronic equipment and road side equipment - Google Patents
Method and device for identifying target area, electronic equipment and road side equipment Download PDFInfo
- Publication number
- CN111709354A CN111709354A CN202010536739.XA CN202010536739A CN111709354A CN 111709354 A CN111709354 A CN 111709354A CN 202010536739 A CN202010536739 A CN 202010536739A CN 111709354 A CN111709354 A CN 111709354A
- Authority
- CN
- China
- Prior art keywords
- identifiers
- distance
- identifier
- target area
- location information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000004590 computer program Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 abstract description 10
- 238000012360 testing method Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 18
- 238000012544 monitoring process Methods 0.000 description 15
- 230000008569 process Effects 0.000 description 9
- 238000001514 detection method Methods 0.000 description 6
- 239000003550 marker Substances 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000012806 monitoring device Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- 239000011575 calcium Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Multimedia (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the disclosure relates to a method and a device for identifying a target area, electronic equipment, a computer-readable storage medium and drive test equipment, and relates to the field of intelligent transportation. The method includes obtaining location information for a set of identifiers detected in a current frame of the video. The method further comprises the steps of determining the distance between every two identifiers in at least a part of the identifiers in the group of the identifiers based on the position information, determining a communication domain based on the distance, wherein the number of the identifiers in the communication domain is larger than a first threshold number, the distance between every two identifiers in the communication domain is smaller than a threshold distance, and determining the area surrounded by the identifiers in the communication domain as the target area. According to the technical scheme, the road condition information can be timely and accurately updated, and the target area information is broadcasted to road side equipment, a cloud platform, vehicles and the like through the vehicle-road cooperation V2X technology, so that reliable data support is provided for path planning of the vehicles, and user experience is improved.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of intelligent transportation, and more particularly, to a method, an apparatus, an electronic device, a computer-readable storage medium, and a drive test device for identifying a target area.
Background
For an autonomous vehicle or other general vehicles, great convenience is provided to a user if a target area (e.g., a construction area, etc.) that may block traffic in front can be found early when planning a driving route of the vehicle. However, for an autonomous vehicle, the sensors carried by itself have a limited range of perception; for a common vehicle, the visual distance and the visual angle of a driver are limited. Therefore, the target area on the road surface cannot be accurately determined, which increases the difficulty in subsequent path planning.
Disclosure of Invention
According to an example embodiment of the present disclosure, a scheme for identifying a target area is provided.
In a first aspect of the present disclosure, a method for identifying a target area is provided. The method may include obtaining location information for a set of identifiers detected in a current frame of the video. The method further includes determining a distance between each two of at least a portion of the identifiers in the set of identifiers based on the location information. The method may further include determining a connectivity domain based on the distance, the connectivity domain including a number of identifiers greater than a first threshold number, the connectivity domain including identifiers having a distance between each two of the identifiers less than a threshold distance. In addition, the method may further include determining a region surrounded by the identifier included in the connected component as the target region.
In a second aspect of the present disclosure, there is provided an apparatus for identifying a target area, comprising: the system comprises a position information acquisition module, a position information acquisition module and a position information acquisition module, wherein the position information acquisition module is configured to acquire the position information of a group of markers detected in a current frame of a video; a distance determination module configured to determine a distance between each two of at least a portion of the identifiers in the set of identifiers based on the location information; a connected component determining module configured to determine a connected component based on the distance, the connected component including a number of identifiers greater than a first threshold number, the connected component including identifiers having a distance between each two identifiers less than a threshold distance; and a target area determination module configured to determine an area surrounded by the identifier included in the connected domain as the target area.
In a third aspect of the disclosure, an electronic device is provided that includes one or more processors; and storage means for storing the one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method according to the first aspect of the disclosure.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements a method according to the first aspect of the present disclosure.
In a fifth aspect of the present disclosure, there is provided a roadside apparatus including the electronic apparatus as described in the third aspect of the present disclosure.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 illustrates a schematic diagram of an example environment in which embodiments of the present disclosure can be implemented;
FIG. 2 shows a schematic diagram of a monitoring scenario according to an embodiment of the present disclosure;
FIG. 3A shows a schematic diagram of a frame in a surveillance video in accordance with another embodiment of the present disclosure;
FIG. 3B shows a schematic view of the respective identifiers identified in the frame of FIG. 3A in a planar coordinate system;
FIG. 4 shows a flow diagram of a process for identifying a target area, according to an embodiment of the present disclosure;
FIG. 5 shows a block diagram of an apparatus for identifying a target area according to an embodiment of the present disclosure; and
FIG. 6 illustrates a block diagram of a computing device capable of implementing various embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
In describing embodiments of the present disclosure, the terms "include" and its derivatives should be interpreted as being inclusive, i.e., "including but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As mentioned above, autonomous vehicles need to acquire information related to road conditions through roadside sensing devices to generate a more rational driving strategy. It is understood that since construction, damage, collapse, etc. may exist near or on a road, it is important for an autonomous vehicle to identify the target area where such a situation occurs in time and to re-plan the driving route.
There are two main types of conventional identification schemes. The first scheme is manual collection and reporting. This is the most primitive but simple and reliable method. Specifically, a specially-assigned person drives a car to patrol, and when the position of a target area is found, the specific position of the target area is acquired through a total station or a handheld GPS, and is recorded and reported. However, this solution is too costly and inefficient, especially for larger-scale cities, performing a full road condition check takes too long, the update rate is too slow, and real-time updating is not supported.
In order to overcome the defects of the first scheme, the second scheme is that pedestrians and drivers report a mode. That is, the target area is reported by means of pedestrians walking on the road every day or drivers of traveling vehicles. However, the coverage of this scheme cannot be guaranteed. Moreover, the situation of false alarm may exist on roads with fewer vehicles and pedestrians.
In recent years, the unmanned technique gradually exposes the corners of the head. More and more enterprises are beginning to invest in unmanned research and development and production. It is anticipated that partially autonomous vehicles will be present on the road for some future time. How to provide reliable road information for these autonomous vehicles is a problem that is urgently needed to be solved at present.
According to an embodiment of the present disclosure, a scheme of identifying a target area is provided. For example, the location information of the identified identifiers may be obtained, and when it is determined that the distance between an identifier and some identifiers is smaller than the threshold distance, it may be determined that the identifier and some identifiers belong to the same connected domain. Identifiers belonging to the same connected-together domain are likely to be used to surround the target area. For a target area there must be a threshold distance for which the smallest of the distances of an identifier from all other identifiers must be less for an identifier at a location in the target area. An area made up of a plurality of identifiers having a distance between the identifiers less than the threshold distance may be generally referred to as a connected domain. The connected domain, i.e., the target region associated therewith, is determined.
Thus, the road surface information can be updated in real time by reporting the position information and the time stamp of the target area and each marker contained therein. In addition, the scheme depends on a road side sensor and an edge computing node in the road side equipment to detect whether a target area exists on the road in real time, and the road side equipment can transmit various information of the detected target area to a cloud platform, other road side equipment, a vehicle and the like through a vehicle-road cooperation v2x technology. In this way, the automatic driving vehicle or other common vehicles can acquire the road information updated in real time, so that great convenience is brought to the planning of the driving path.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. Fig. 1 illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. As shown in FIG. 1, an example environment 100 includes a surveillance video, a computing device 120, and a target area 130. The surveillance video includes a plurality of frames, such as frame 110 and frame 111 preceding frame 110. For convenience of description, the frame 110 is hereinafter also referred to as "current frame 110", and the frame 111 is also referred to as "previous frame 111". The target area 130 is used to indicate an area where a vehicle including a vehicle cannot pass, which may affect the planning of a driving route.
As shown in fig. 1, the computing device 120 identifies a target area 130 by processing the surveillance video. In some embodiments, the computing device 120 may be located in the cloud for identifying the identifier for surrounding the target area according to a particular recognition model. It should be understood that "marker" as described herein refers to an object having a distinct topographical feature for identifying a target area (such as, a construction area, a collapsed area, etc.), which may be a cone or a cylindrical barrel with a speckled pattern, or the like. In some embodiments, computing device 120 may include, but is not limited to, a personal computer, a server computer, a hand-held or laptop device, a mobile device (such as a mobile phone, a Personal Digital Assistant (PDA), a media player, etc.), a multiprocessor system, a consumer electronics, a minicomputer, a mainframe computer, a distributed computing environment that includes any of the above systems or devices, and the like.
Through processing by the computing device 120, a particular portion of the current frame 110 may be determined to be the target region 130. For example, each frame in the video may be monitored in real time, and when an identifier is found to be present in the current frame 110, the location information for each identifier may be determined. When an identifier is determined to have a distance to some identifiers that is less than a threshold distance, the identifier and some identifiers may be determined to belong to the same communication domain. Thus, the area surrounded by the identifier belonging to the connected domain can be determined as the target area 130.
In this way, road surface information may be updated in real time by reporting the location information and time stamp of the target area 130 and each identifier contained therein. Because the road surface information can be updated in time, the automatic driving vehicle or other common vehicles can realize a more accurate and convenient path planning scheme.
Fig. 2 shows a schematic diagram of a monitoring scenario 200 according to an embodiment of the present disclosure. In the monitoring scenario 200, a drive test device including the drive test monitoring apparatus 210 and the computing device 120 exists, and the computing device 120 may be in communication connection with the drive test monitoring apparatus 210 in a wired or wireless manner. As shown in fig. 2, the roadside monitoring unit 210 may be provided at a location such as a monitor for taking a photo of violation for monitoring road surface information of a substantially straight road. It should be understood that the path is usually monitored by a plurality of drive test monitoring apparatuses at different positions, and different monitoring results may be associated and fused by the computing device 120, and finally the target area is determined. The road side device may report various information of the detected target area to the cloud platform through a vehicle-road cooperation v2x technology, or broadcast the information to other road side devices or vehicles.
As shown in fig. 2, the roadside monitoring unit 210 may photograph the markers such as the pyramid buckets 221, 222. It should be understood that the roadside monitoring device 210 may also photograph moving objects (not shown) such as people, bicycles, motorcycles, and the like. It should also be understood that for ease of discussion, the arrangement shown in fig. 2 of the present disclosure shows only the cone barrels 221, 222. However, the present disclosure is not limited to monitoring the cone barrels 221, 222 using the roadside monitoring device 210, and other markers (e.g., cylindrical barrels with speckle patterns, etc.) that are present in the monitoring scene of the roadside monitoring device 210 to indicate the target area may all be monitored simultaneously by the roadside monitoring device 210.
One frame of the monitoring video captured by the roadside monitoring device 210 in fig. 2 will be described in detail below. Fig. 3A shows a schematic diagram of a frame 300A in surveillance video according to another embodiment of the present disclosure. In FIG. 3A, identifiers 310-1, 310-2, 310-3, 310-4, and 310-5 (hereinafter collectively 310) are present in frame 300A. It is generally straightforward for a person to determine that a target region consisting of the identifiers 310-1, 310-2, 310-3, 310-4 is present in the frame 300A, and that the identifier 310-5 is isolated from the target region. However, to conserve human resources, the computing device 120 is configured to assume the task of identifying the target area.
To detect the identifier 310 from the frame 300A, the computing device 120 first identifies the identifier 310 in the frame 300A. Fig. 3B is a schematic diagram showing a positional relationship diagram 300B of each marker identified in the frame of fig. 3A in a planar coordinate system. It is to be understood that the positional relationship diagram 300B is obtained by two-dimensionally spatially converting the frame 300A. In fig. 3B, the position relation diagram 300B may be detected by using a two-dimensional object detection model trained in advance. For example, when the identifier 310 is detected to be present in the positional relationship diagram 300B, a detection frame may be added to the identifier 310 (e.g., a frame surrounding the identifier 310 in fig. 3B). It should be understood that the rectangular detection frame is merely exemplary, and that the detection frame may also be circular, diamond-shaped, or shaped to just encompass the outer perimeter of the marker, etc., as desired.
The technical solutions described above are only used for illustration and do not limit the invention. It should be understood that the entire monitoring system may also be arranged in other ways and connections. In order to explain the principle of the above scheme more clearly, the process of identifying the target area will be described in more detail below with reference to fig. 4.
Fig. 4 shows a flow diagram of a process 400 for identifying a target area, according to an embodiment of the present disclosure. In some embodiments, the method 400 may be implemented in the computing device 120 shown in FIG. 1. A process 400 for identifying a target area according to an embodiment of the present disclosure is now described with reference to fig. 4. For ease of understanding, specific data mentioned in the following description are exemplary and are not intended to limit the scope of the present disclosure.
At 402, computing device 120 may obtain location information for a set of identifiers 310 detected in current frame 110 of the video. By way of example, if computing device 120 determines that the number of identifiers 310 in current frame 110 is greater than or equal to a threshold number, it is indicative that these identifiers may be used to surround the target area. Thus, the computing device 120 may determine the location information of the identifiers 310. In this way, the identifier surrounding the target region can be accurately determined in combination with the a priori information. Alternatively or additionally, if computing device 120 determines that the number of identifiers 310 in a predetermined number of consecutive frames in the video (consecutive previous frames 111 up to current frame 110, as shown in fig. 1) is greater than or equal to a threshold number, then it is indicated that these identifiers 310 are stably present in the video for a period of time, and that these identifiers 310 may be used to surround a target region. Thus, the computing device 120 may determine the location information of the identifiers 310. In this way, unstable recognition results can be filtered out.
In some embodiments, the identifier may be a cone-barrel as shown in fig. 2, 3A, and 3B. Alternatively or additionally, the marker may also be a cylindrical barrel, a road-block ball or the like having a road-block function and having a specific shape and color. Such identifiers each have a particular shape that facilitates identification by the computing device 120.
At 404, the computing device 120 may determine a distance between each two of at least a portion of the identifiers in the set of identifiers 310 based on the location information. By way of example, the computing device 120 may traverse the distance between the identifier 310-1 of the identifiers 310 and each of the other identifiers, the distance between the identifier 310-2 of the identifiers 310 and each of the other identifiers, the distance between the identifier 310-3 of the identifiers 310 and each of the other identifiers, the distance between the identifier 310-4 of the identifiers 310 and each of the other identifiers, and the distance between the identifier 310-5 of the identifiers 310 and each of the other identifiers.
In certain embodiments, if a first distance between a first identifier and a second distance between the first identifier and a third identifier in a set of identifiers are both less than a threshold distance, the distance between the second identifier and the third identifier is no longer determined. By way of example, if the computing device 120 discovers that the identifiers 310-1 and 310-2, 310-3, and 310-4 are each less than the threshold distance when calculating the distance between the identifier 310-1 of the identifiers 310 and each of the other identifiers, the computing device 120 may omit calculating the distances between the identifier 310-2 and the identifiers 310-1, 310-3, and 310-4 when calculating the distances between the identifier 310-2 of the identifier 310 and each of the other identifiers. More generally, assuming a total of (m1+ m2+ … mn) identifiers, n being the number of target regions, the number of original traversal calculations is given by the following equation:
Calculate_times1＝((m1+m2+…+mn)2-(m1+m2+…+mn))/2，
the number of calculations shown in this embodiment is given by the following formula two:
Calculate_times2＝
calcium _ times1- [ (m1-2) + (m1-3) + … +1] - … - [ (mn-2) + (mn-3) + … +1 ]. It can be seen that the degree of saving of computational power by the computational method shown in the present embodiment is proportional to the square of the number of target regions. In this way, computing device 120 may be saved from computational effort.
At 406, computing device 120 may determine a connected domain based on the distance determined above. The connected component includes a number of identifiers greater than a first threshold number, e.g., 4. Further, the connected component includes identifiers, each two of which are separated by a distance that is less than a threshold distance. For a target area there must be a threshold distance for which the smallest of the distances of an identifier from all other identifiers must be less for an identifier at a location in the target area. That is, for an identifier, if the identifier is present in a target area, then there must be another identifier in the target area such that the distance between the two identifiers is minimized. Additionally or alternatively, the threshold distance may be artificially determined based on a priori information.
At 408, the computing device 120 may determine an area surrounded by the identifier included in the connected component as the target area 130. In this manner, the computing device 120 enables identification of a target area surrounded by the identifiers by identifying the plurality of identifiers and determining the connected components by performing distance calculations based on their location information. In this way, the computing device 120 may update the road condition information accurately in time, thereby providing reliable data support for path planning of the vehicle.
In some embodiments, the computing device 120 may also compare the determined current contour of the target region 130 to a previous contour of a previous target region determined based on a previous frame 111 in the video. If the ratio of overlap of the current contour and the previous contour is less than or equal to a threshold ratio (e.g., 80%), the previous target region may be updated with the target region 130, otherwise, the target region 130 is unchanged. Specifically, the computing device 120 may traverse all previous contours already existing, find the previous contour that most coincides with the current contour, and note the repetition rate between the two. In this manner, the computing device 120 may determine whether the newly determined target area has ever changed and report the change.
It should be understood that the target area identification method of the present disclosure has an advantage over the conventional identification method in that the target area identification method of the present disclosure does not need to acquire and report information by manual means as in the conventional method whenever a new target area appears in a monitored video. The reason is that the present disclosure finds a plurality of markers suitable for composing the target area by determining the connected domain, thereby completing the identification of the target area. Therefore, the road provided with the monitoring camera equipment can be fully covered on the premise of not carrying out too much manual intervention, timely and accurate road information is provided for a user, the human resource cost is saved, and the user experience is improved.
Fig. 5 shows a block diagram of an apparatus 500 for identifying a target area 130 according to an embodiment of the present disclosure. As shown in fig. 5, the apparatus 500 may include: a location information obtaining module 502 configured to determine location information of a set of markers detected in a current frame of the video; a distance determination module 504 configured to determine a distance between each two of at least a portion of the identifiers in the set of identifiers based on the location information; a connected component determination module 506 configured to determine a connected component based on the distance, the connected component including a number of identifiers greater than a first threshold number, the connected component including identifiers having a distance between each two identifiers less than a threshold distance; and a target area determination module 508 configured to determine an area surrounded by the identifier included in the connected component as a target area.
In certain embodiments, the location information acquisition module 502 is configured to: determining the location information if the number of the set of identifiers in a predetermined number of consecutive frames in the video is determined to be greater than or equal to a second threshold number.
In certain embodiments, the location information acquisition module 502 is configured to: determining the location information if the number of the set of identifiers in the current frame is determined to be greater than or equal to a third threshold number.
In some embodiments, the apparatus may further comprise: a contour comparison module configured to compare the determined current contour of the target region to a previous contour of a previous target region determined based on a previous frame in the video; and an update module configured to update the previous target region with the target region if a ratio of overlap of the current contour and the previous contour is less than or equal to a threshold ratio.
In certain embodiments, if a first distance between a first identifier and a second distance between the first identifier and a third identifier in the set of identifiers are both less than a threshold distance, the distance between the second identifier and the third identifier is no longer determined.
In some embodiments, the identifier may include at least one of a cone, a cylinder, and a barricade ball.
FIG. 6 illustrates a block diagram of a computing device 600 capable of implementing multiple embodiments of the present disclosure. Device 600 may be used to implement computing device 120 of fig. 1. As shown, device 600 includes a Central Processing Unit (CPU)601 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The CPU601, ROM602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 504.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processing unit 601 performs the various methods and processes described above, such as the processes 200, 300, and 400. For example, in some embodiments, processes 200, 300, and 400 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by CPU601, one or more of the steps of processes 200, 300, and 400 described above may be performed. Alternatively, in other embodiments, CPU601 may be configured to perform processes 200, 300, and 400 by any other suitable means (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (15)
1. A method for identifying a target area, comprising:
acquiring position information of a group of markers detected in a current frame of a video;
determining a distance between each two of at least a portion of the identifiers in the set of identifiers based on the location information;
determining a connected domain based on the distance, wherein the connected domain comprises a number of identifiers which is larger than a first threshold number, and the distance between every two identifiers in the identifiers which are comprised in the connected domain is smaller than a threshold distance; and
determining an area surrounded by the identifier included in the connected domain as the target area.
2. The method of claim 1, wherein obtaining location information for the set of identifiers comprises:
determining the location information if the number of the set of identifiers in a predetermined number of consecutive frames in the video is determined to be greater than or equal to a second threshold number.
3. The method of claim 1, wherein obtaining location information for the set of identifiers comprises:
determining the location information if the number of the set of identifiers in the current frame is determined to be greater than or equal to a third threshold number.
4. The method of claim 1, further comprising:
comparing the determined current contour of the target region to a previous contour of a previous target region determined based on a previous frame in the video; and
updating the previous target area with the target area if a ratio of overlap of the current contour and the previous contour is less than or equal to a threshold ratio.
5. The method of claim 1, wherein if a first distance between a first identifier and a second identifier in the set of identifiers and a second distance between the first identifier and a third identifier are both less than a threshold distance, a distance between the second identifier and the third identifier is no longer determined.
6. The method of claim 1, wherein the set of identifiers comprises at least one of:
a conical barrel;
a cylindrical barrel; and
a road block ball.
7. An apparatus for identifying a target area, comprising:
the system comprises a position information acquisition module, a position information acquisition module and a position information acquisition module, wherein the position information acquisition module is configured to acquire the position information of a group of markers detected in a current frame of a video;
a distance determination module configured to determine a distance between each two of at least a portion of the identifiers in the set of identifiers based on the location information;
a connected component determining module configured to determine a connected component based on the distance, the connected component including a number of identifiers greater than a first threshold number, the connected component including identifiers having a distance between each two identifiers less than a threshold distance; and
a target area determination module configured to determine an area surrounded by the identifier included in the connected domain as the target area.
8. The apparatus of claim 7, wherein the location information acquisition module is configured to:
determining the location information if the number of the set of identifiers in a predetermined number of consecutive frames in the video is determined to be greater than or equal to a second threshold number.
9. The apparatus of claim 7, wherein the location information acquisition module is configured to:
determining the location information if the number of the set of identifiers in the current frame is determined to be greater than or equal to a third threshold number.
10. The apparatus of claim 7, further comprising:
a contour comparison module configured to compare the determined current contour of the target region to a previous contour of a previous target region determined based on a previous frame in the video; and
an update module configured to update the previous target region with the target region if a ratio of overlap of the current contour and the previous contour is less than or equal to a threshold ratio.
11. An apparatus according to claim 7, wherein if a first distance between a first identifier and a second identifier in the set of identifiers and a second distance between the first identifier and a third identifier are both less than a threshold distance, a distance between the second identifier and the third identifier is no longer determined.
12. The apparatus of claim 7, wherein the set of identifiers comprises at least one of:
a conical barrel;
a cylindrical barrel; and
a road block ball.
13. An electronic device, the electronic device comprising:
one or more processors; and
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1-6.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
15. A roadside apparatus comprising the electronic apparatus of claim 13.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010536739.XA CN111709354B (en) | 2020-06-12 | 2020-06-12 | Method and device for identifying target area, electronic equipment and road side equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010536739.XA CN111709354B (en) | 2020-06-12 | 2020-06-12 | Method and device for identifying target area, electronic equipment and road side equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111709354A true CN111709354A (en) | 2020-09-25 |
| CN111709354B CN111709354B (en) | 2023-07-28 |
Family
ID=72540256
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010536739.XA Active CN111709354B (en) | 2020-06-12 | 2020-06-12 | Method and device for identifying target area, electronic equipment and road side equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111709354B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114137977A (en) * | 2021-11-29 | 2022-03-04 | 北京云迹科技有限公司 | Moving method, robot, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140160295A1 (en) * | 2012-12-06 | 2014-06-12 | Honda Motor Co., Ltd. | Road condition detection |
| CN108628206A (en) * | 2017-03-17 | 2018-10-09 | 通用汽车环球科技运作有限责任公司 | Road construction detecting system and method |
| CN110991320A (en) * | 2019-11-29 | 2020-04-10 | 北京百度网讯科技有限公司 | Road condition detection method and device, electronic equipment and storage medium |
| CN111063192A (en) * | 2019-12-31 | 2020-04-24 | 戴姆勒股份公司 | Operating area compliance judgment method and system |
-
2020
- 2020-06-12 CN CN202010536739.XA patent/CN111709354B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140160295A1 (en) * | 2012-12-06 | 2014-06-12 | Honda Motor Co., Ltd. | Road condition detection |
| CN108628206A (en) * | 2017-03-17 | 2018-10-09 | 通用汽车环球科技运作有限责任公司 | Road construction detecting system and method |
| CN110991320A (en) * | 2019-11-29 | 2020-04-10 | 北京百度网讯科技有限公司 | Road condition detection method and device, electronic equipment and storage medium |
| CN111063192A (en) * | 2019-12-31 | 2020-04-24 | 戴姆勒股份公司 | Operating area compliance judgment method and system |
Non-Patent Citations (1)
| Title |
|---|
| 齐麟: "面向低速自动驾驶汽车的路径规划决策" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114137977A (en) * | 2021-11-29 | 2022-03-04 | 北京云迹科技有限公司 | Moving method, robot, equipment and storage medium |
| CN114137977B (en) * | 2021-11-29 | 2024-02-27 | 北京云迹科技股份有限公司 | Moving method, robot, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111709354B (en) | 2023-07-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110287276B (en) | High-precision map updating method and device and storage medium | |
| US10339669B2 (en) | Method, apparatus, and system for a vertex-based evaluation of polygon similarity | |
| CN108345822B (en) | Point cloud data processing method and device | |
| US20220373353A1 (en) | Map Updating Method and Apparatus, and Device | |
| CN105793669B (en) | Vehicle position estimation system, device, method, and camera device | |
| CN106097755A (en) | For identifying the method parked place and/or vacate place | |
| CN110785774A (en) | Method and system for closed loop sensing in autonomous vehicles | |
| US20210272450A1 (en) | Method of providing traffic related information and device, computer program and computer program product | |
| CN110753953A (en) | Method and system for object-centric stereo vision in autonomous vehicles via cross-modality verification | |
| CN110869559A (en) | Method and system for integrated global and distributed learning in autonomous vehicles | |
| JP2023508705A (en) | Data transmission method and device | |
| CN109872530B (en) | Road condition information generation method, vehicle-mounted terminal and server | |
| WO2021253245A1 (en) | Method and device for identifying vehicle lane changing tendency | |
| CN107730993A (en) | The parking lot intelligent vehicle-tracing system and method identified again based on image | |
| CN108594244B (en) | Obstacle recognition transfer learning method based on stereoscopic vision and laser radar | |
| US20200162724A1 (en) | System and method for camera commissioning beacons | |
| CN112101223B (en) | Detection method, detection device, detection equipment and computer storage medium | |
| CN111458721A (en) | Exposed garbage identification and positioning method, device and system | |
| US20230126901A1 (en) | Method, apparatus, server, and computer program for collision accident prevention | |
| CN110765224A (en) | Processing method of electronic map, vehicle vision repositioning method and vehicle-mounted equipment | |
| CN112418081A (en) | Method and system for air-ground joint rapid investigation of traffic accidents | |
| CN114565906A (en) | Obstacle detection method, obstacle detection device, electronic device, and storage medium | |
| CN111709354B (en) | Method and device for identifying target area, electronic equipment and road side equipment | |
| CN111709357B (en) | Method and device for identifying target area, electronic equipment and road side equipment | |
| CN111709355B (en) | Method and device for identifying target area, electronic equipment and road side equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20211020 Address after: 100176 101, floor 1, building 1, yard 7, Ruihe West 2nd Road, Beijing Economic and Technological Development Zone, Daxing District, Beijing Applicant after: Apollo Intelligent Connectivity (Beijing) Technology Co., Ltd. Address before: 100094 2 / F, baidu building, No.10 Shangdi 10th Street, Haidian District, Beijing Applicant before: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |